The testing results are shown in Tab. REF . The proposed PatchNet achieves competitive results on all protocols. Due to the high variance of capture types in C dataset, it is hard to learn robust local features to address both high and low resolution scenarios. We also conduct an ablation study in this benchmark to explore the influence of each component in our framework and show the results in Tab. REF . In the first ablation experiment, we split the patch classes using the strategy proposed by SSDG [1]}: It aggregates the live samples as one class and treats spoof samples from each other dataset as one class, which results in 4 classes. The results verify that the proposed fine-grained class split, \(L_{Asym}\) , and \(L_{Sim}\)  are all important to regularize the network to generalize better in challenging FAS tasks.
